Dominant SIFT: A novel compact descriptor

Anh T. Tra; Weisi Lin; Alex C. Kot

ICASSP 2015accepted0 citations

Dominant SIFT: A novel compact descriptor

Abstract

Definition and extraction of local features play a very important role in image retrieval (IR), pattern recognition and computer vision. Fast growth of technology today calls for local features to be as compact as possible toward real-time and limited bandwidth applications. In this paper, we study the problem of representing images in a compact way to achieve low bit-rate transmission while maintaining good performance. To be more specific, we propose a novel compact descriptor, dominant SIFT, which only uses 48 bits to describe local features. Importantly, our descriptor is training-free, vocabulary-free and suitable for real-time and mobile applications. We show the effectiveness of the proposed compact descriptor in image retrieval.

BibTeX

@inproceedings{icassp2015_dominantsiftanov,
  title = {Dominant SIFT: A novel compact descriptor},
  author = {Anh T. Tra and Weisi Lin and Alex C. Kot},
  booktitle = {ICASSP 2015},
  year = {2015}
}